Establishment of a Monitoring Model for the Cotton Leaf Area Index Based on the Canopy Reflectance Spectrum

Fan, Xianglong; Lv, Xin; Gao, Pan; Zhang, Lifu; Zhang, Ze; Zhang, Qiang; Ma, Yiru; Xiang, Yi; Chen, Yin; Ma, Lulu

doi:10.3390/land12010078

Cited by 4 publications

(3 citation statements)

References 39 publications

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“…is a significant commercial crop worldwide and serves as the primary source of natural textile fiber. In China, cotton holds a crucial strategic position and has played a pivotal role in increasing agricultural output and revenue [1]. However, due to limited arable land availability, expanding the area dedicated to cotton cultivation is challenging.…”

Section: Introductionmentioning

confidence: 99%

Straw Returning Measures Enhance Soil Moisture and Nutrients and Promote Cotton Growth

Tang

Luo

et al. 2023

Agronomy

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In order to investigate the comprehensive effects of straw returning on soil physical and chemical properties, as well as cotton growth in Jiangsu, China, and to determine suitable high-yield and efficient straw returning measures, this study implemented three different straw returning methods: straw mulching (SM), straw incorporation (SI), and straw biochar (BC), with no straw returning served as a control (CT). The study aimed to assess the impact of these straw-returning measures on soil nutrients, soil moisture content, soil water storage, and deficit status, as well as primary indicators of cotton growth. The findings revealed that the total available nutrient storage under SM, SI, and BC showed an increase of 11.93%, 11.15%, and 32.39%, respectively, compared to CT. Among these methods, BC demonstrated a significant enhancement in soil organic carbon content, available phosphorus, and available potassium. Furthermore, SM exhibited a considerable increase in soil moisture content across all layers (0–40 cm), resulting in an average water storage increase of 7.42 mm compared to CT. Consequently, this effectively reduced the soil water deficit during the cotton development period. Moreover, the height of cotton plants was increased by SM, SI, and BC, with SM promoting the greatest growth rate of up to 66.87%. SM resulted in an 11.17 cm increase in cotton plant height compared to CT. Additionally, SM contributed to higher chlorophyll content in leaves at the end of the growth period. Overall, the indicators suggest that straw mulching is particularly effective in enhancing soil moisture and nutrient distribution, especially during dry years, and has a positive impact on promoting cotton development. Based on the results, straw mulching emerges as a recommended straw-returning measure for improving soil quality and maximizing cotton production in the study area.

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Section: Introductionmentioning

confidence: 99%

Straw Returning Measures Enhance Soil Moisture and Nutrients and Promote Cotton Growth

Tang

Luo

et al. 2023

Agronomy

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“…There are five commonly used visible-light vegetation indices: the red-to-green ratio index (RGRI) [44], excess green index (EXG) [45,46], visible light band difference vegetation index (VDVI) [47][48][49], normalized green-red difference index (NGRDI) [50], and normalized green-blue difference index (NGBDI) [51]. Among them, the VDVI has the highest overall accuracy rate of 97.47% [40], which indicates that the visible-wave vegetation index can be used to calculate vegetation coverage and has the highest applicability.…”

Section: Discussionmentioning

confidence: 99%

Research on Evaluating the Characteristics of the Rural Landscape of Zhanqi Village, Chengdu, China, Based on Oblique Aerial Photography by Unmanned Aerial Vehicles

Zhu,

Li,

Luo

et al. 2024

Sustainability

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To achieve the transition of rural areas from traditional to modern, the visualization of rural landscape data and feature evaluations are essential. Landscape character assessment (LCA) is a well-established tool that was developed to assess and understand rural landscape features. In recent years, drones have become increasingly attractive for various applications and services due to their low costs and relative ease of operation. Unlike most previous studies that relied solely on drone-based remote sensing or visual esthetic evaluations, this study proposes an innovative assessment method based on landscape characteristic assessment (LCA) and oblique drone photography technology, supported by specific data and survey results. These include various landscape metrics, such as the Shannon diversity index (SHDI), Shannon evenness index (SHEI), vegetation coverage, landscape character zoning, and delineations of various ecologically sensitive areas. This method was applied to study Zhanqi Village in Chengdu, Sichuan Province, China and revealed some unique characteristics of this village. By categorizing and describing the landscape features, the study makes judgments and decisions about them. This is a beneficial attempt to apply the scientific methods of landscape assessments to the production management of aerial drone surveys. This method provides a comprehensive framework for evaluating rural landscape features and demonstrates that the combination of LCA and oblique drone photography technology is feasible for rural landscape research. Additionally, this study emphasizes the need for further research to explore the potential application of this method in continuously evolving urban and rural environments in the future.

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“…For correct nutritional diagnosis and cotton canopy structure and unharmed growth. For correct nutritional diagnosis and cotton fertilization, precise cotton LAI monitoring is important (Fan et al, 2022).…”

Section: Canopy Architecture and Leaf Area Indexmentioning

confidence: 99%

The Consequences of Plant Architecture and Spatial Distribution of Light Interception on Cotton Growth and Yield

2023

IJAB

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The architecture of the canopy tends to affect how light is reflected and distributed within it. Rational modelling and trimming can improve crop architecture, maximize the use of space, light, and resources such as land, and lay the groundwork for initial maturing, and maximum yield. Determining the interception of light inside the canopy is critical aimed at increasing the population's photosynthetic production. By implementing cultural practices that produce optimal plant populations and alter the plant canopy components, it is possible to maximize light utilization in the production of cotton. In order to forecast the expected yield for uses like crop management and agronomic decision-making, as well as to investigate potential impacts of environmental alteration on food security, crop growth models are used to estimate the correlation between plants and the environment. In this study, we highlight the light interception, canopy architecture and their use in crop growth models to improve crop productivity. Constructing a strong technological system capable of phenotyping crops in a high-throughput, multidimensional, large-data, efficient, and mechanically determining manner is the ultimate objective.

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Establishment of a Monitoring Model for the Cotton Leaf Area Index Based on the Canopy Reflectance Spectrum

Cited by 4 publications

References 39 publications

Straw Returning Measures Enhance Soil Moisture and Nutrients and Promote Cotton Growth

Straw Returning Measures Enhance Soil Moisture and Nutrients and Promote Cotton Growth

Research on Evaluating the Characteristics of the Rural Landscape of Zhanqi Village, Chengdu, China, Based on Oblique Aerial Photography by Unmanned Aerial Vehicles

The Consequences of Plant Architecture and Spatial Distribution of Light Interception on Cotton Growth and Yield

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